Physio Protocol
ISDP Quality Analysis
Elisa Ugarte
9/21/2024
Z score anthro Birth
## [1] "birth_bfeed_prob_1" "birth_condition_modified"
## [3] "average_birth_circum" "average_birth_length"
## [5] "average_birth_muac" "average_birth_weight"
## [7] "cry_birth" "baby_issue_birth"
## [9] "child_gender_final" "hhid_int"
## [11] "days_b"## ================================================================================## ================================================================================## ================================================================================## ================================================================================## # A tibble: 3 × 2
## underweight n
## <dbl> <int>
## 1 0 98
## 2 1 10
## 3 NA 34## # A tibble: 3 × 2
## stunting n
## <dbl> <int>
## 1 0 102
## 2 1 5
## 3 NA 35## # A tibble: 3 × 2
## wasting n
## <dbl> <int>
## 1 0 93
## 2 1 12
## 3 NA 37##
## Low Birth Weight Normal Birth Weight
## 15 93##
## 0 1
## Low Birth Weight 5 10
## Normal Birth Weight 93 0## # A tibble: 142 × 20
## birth_bfeed_prob_1 birth_condition_modified average_birth_circum
## <dbl+lbl> <dbl> <dbl>
## 1 NA NA NA
## 2 NA NA NA
## 3 0 [No] 0 32
## 4 0 [No] 1 35.0
## 5 0 [No] 1 33.4
## 6 0 [No] 1 34.0
## 7 0 [No] 1 33.4
## 8 0 [No] 1 34.2
## 9 0 [No] 1 33.8
## 10 0 [No] 1 34.0
## average_birth_length average_birth_muac average_birth_weight cry_birth
## <dbl> <dbl> <dbl> <dbl+lbl>
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 47.7 9.3 2.46 1 [Immediate]
## 4 47.0 8.04 2.83 2 [Delayed]
## 5 47.1 8.85 2.49 1 [Immediate]
## 6 50.8 11.2 3.4 1 [Immediate]
## 7 48.0 9.9 2.48 1 [Immediate]
## 8 52.8 10.4 3.08 1 [Immediate]
## 9 50.8 9.55 3.06 2 [Delayed]
## 10 50.0 10.0 2.89 1 [Immediate]
## baby_issue_birth child_gender_final hhid_int days_b wflz wfaz lfaz hcaz
## <dbl> <dbl+lbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA 2213 2 NA NA NA NA
## 2 NA NA 1152 2 NA NA NA NA
## 3 1 2 [Female] 2514 2 -1.96 -1.75 -0.98 -1.76
## 4 1 1 [Male] 880 2 0.21 -1.1 -1.68 0.27
## 5 1 2 [Female] 1170 2 -1.4 -1.67 -1.28 -0.57
## 6 0 2 [Female] 1294 2 -0.38 0.4 0.68 -0.02
## 7 0 1 [Male] 2197 2 -2 -1.95 -1.15 -0.96
## 8 1 1 [Male] 597 2 -2.98 -0.53 1.35 -0.37
## 9 1 1 [Male] 2036 2 -1.52 -0.57 0.3 -0.69
## 10 0 1 [Male] 1321 2 -1.62 -0.95 -0.1 -0.49
## stunting underweight wasting weight4length weight_category
## <dbl> <dbl> <dbl> <dbl> <chr>
## 1 NA NA NA NA <NA>
## 2 NA NA NA NA <NA>
## 3 0 0 0 0.0516 Low Birth Weight
## 4 0 0 0 0.0602 Normal Birth Weight
## 5 0 0 0 0.0529 Low Birth Weight
## 6 0 0 0 0.0670 Normal Birth Weight
## 7 0 0 0 0.0515 Low Birth Weight
## 8 0 0 1 0.0584 Normal Birth Weight
## 9 0 0 0 0.0603 Normal Birth Weight
## 10 0 0 0 0.0578 Normal Birth Weight
## # ℹ 132 more rows## [1] "hhid_int" "wflz_birth" "wfaz_birth"
## [4] "lfaz_birth" "hcaz_birth" "stunting_birth"
## [7] "underweight_birth" "wasting_birth" "weight4length_birth"
## [10] "weight_category_birth"WHO 28 days
## ================================================================================## ================================================================================## ================================================================================## ================================================================================##
## 0 1
## 102 17##
## 0 1
## 112 7##
## 0 1
## 99 20## # A tibble: 142 × 17
## average_baby_circum average_baby_length.y average_baby_muac.y
## <dbl> <dbl> <dbl>
## 1 NA NA NA
## 2 NA NA NA
## 3 36 54.8 10.8
## 4 38.0 52.2 12.0
## 5 36.4 54.7 10.9
## 6 36.5 56.2 12.1
## 7 37.4 55.3 12.4
## 8 37 56 12.6
## 9 36.9 57.0 12.4
## 10 36.7 55.6 11.5
## average_baby_weight.y average_baby_circum_plausible.y baby_issue_birth
## <dbl> <dbl> <dbl>
## 1 NA NA NA
## 2 NA NA NA
## 3 3.50 36 1
## 4 3.94 38.0 1
## 5 3.95 36.4 1
## 6 4.39 36.5 0
## 7 4.66 37.4 0
## 8 4.57 37 1
## 9 4.58 36.9 1
## 10 4.42 36.7 0
## child_gender_final hhid_int child_age_final wflz wfaz lfaz hcaz stunting
## <dbl+lbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NA 2213 NA NA NA NA NA NA
## 2 NA 1152 NA NA NA NA NA NA
## 3 2 [Female] 2514 37.4 -2.87 -1.68 0.16 -0.82 0
## 4 1 [Male] 880 35.4 0.37 -1.25 -1.6 0.38 0
## 5 2 [Female] 1170 52.4 -1.39 -1.59 -0.73 -1.18 0
## 6 2 [Female] 1294 42.4 -1.16 -0.31 0.55 -0.65 0
## 7 1 [Male] 2197 50.4 0.08 -0.87 -0.96 -0.93 0
## 8 1 [Male] 597 53.4 -0.66 -1.18 -0.78 -1.46 0
## 9 1 [Male] 2036 48.4 -1.34 -0.9 -0.01 -1.3 0
## 10 1 [Male] 1321 50.4 -0.69 -1.28 -0.83 -1.61 0
## underweight wasting weight4length
## <dbl> <dbl> <dbl>
## 1 NA NA NA
## 2 NA NA NA
## 3 0 1 0.0640
## 4 0 0 0.0754
## 5 0 0 0.0722
## 6 0 0 0.0782
## 7 0 0 0.0843
## 8 0 0 0.0817
## 9 0 0 0.0805
## 10 0 0 0.0796
## # ℹ 132 more rows## [1] "hhid_int" "wflz_28" "wfaz_28" "lfaz_28"
## [5] "hcaz_28" "stunting_28" "underweight_28" "wasting_28"
## [9] "weight4length_28"Behavioral indicators and RSA
data %>% dplyr::select(bgc_baseline_n1_1_28, bgc_baseline_n2_1_28, bgm_baseline_n1_1_28, bgm_baseline_n2_1_28) %>% report_table()## Variable | Level | n_Obs | percentage_Obs
## -------------------------------------------------------
## bgc_baseline_n1_1_28 | 1 | 21 | 14.79
## bgc_baseline_n1_1_28 | 2 | 8 | 5.63
## bgc_baseline_n1_1_28 | 3 | 20 | 14.08
## bgc_baseline_n1_1_28 | 4 | 14 | 9.86
## bgc_baseline_n1_1_28 | 5 | 43 | 30.28
## bgc_baseline_n1_1_28 | 6 | 11 | 7.75
## bgc_baseline_n1_1_28 | missing | 25 | 17.61
## bgc_baseline_n2_1_28 | 1 | 18 | 12.68
## bgc_baseline_n2_1_28 | 2 | 15 | 10.56
## bgc_baseline_n2_1_28 | 3 | 16 | 11.27
## bgc_baseline_n2_1_28 | 4 | 12 | 8.45
## bgc_baseline_n2_1_28 | 5 | 50 | 35.21
## bgc_baseline_n2_1_28 | 6 | 6 | 4.23
## bgc_baseline_n2_1_28 | missing | 25 | 17.61
## bgm_baseline_n1_1_28 | 1 | 17 | 11.97
## bgm_baseline_n1_1_28 | 2 | 12 | 8.45
## bgm_baseline_n1_1_28 | 3 | 27 | 19.01
## bgm_baseline_n1_1_28 | 4 | 11 | 7.75
## bgm_baseline_n1_1_28 | 5 | 35 | 24.65
## bgm_baseline_n1_1_28 | 6 | 15 | 10.56
## bgm_baseline_n1_1_28 | missing | 25 | 17.61
## bgm_baseline_n2_1_28 | 1 | 21 | 14.79
## bgm_baseline_n2_1_28 | 2 | 16 | 11.27
## bgm_baseline_n2_1_28 | 3 | 29 | 20.42
## bgm_baseline_n2_1_28 | 4 | 5 | 3.52
## bgm_baseline_n2_1_28 | 5 | 34 | 23.94
## bgm_baseline_n2_1_28 | 6 | 12 | 8.45
## bgm_baseline_n2_1_28 | missing | 25 | 17.61data %>%
mutate(across(c(bgc_baseline_n1_1_28, bgc_baseline_n2_1_28, bgm_baseline_n1_1_28,
bgm_baseline_n2_1_28, fussiness_1_28), as.numeric)) %>%
# Calculate the average for bgc (Baseline Galvanic Conductance)
mutate(bgc_avg = (bgc_baseline_n1_1_28 + bgc_baseline_n2_1_28) / 2,
# Calculate the average for bgm (Baseline Galvanic Movement)
bgm_avg = (bgm_baseline_n1_1_28 + bgm_baseline_n2_1_28) / 2,
# Calculate the overall average of bgc and bgm
bgc_bgm_avg = (bgc_avg + bgm_avg) / 2) %>%
correlation(p_adjust = "none", select = c("RSA_alone", "RSA_tog"), select2 = c("bgc_avg", "bgm_avg")) ## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t | df | p
## ------------------------------------------------------------------------
## RSA_alone | bgc_avg | -0.33 | [-0.51, -0.13] | -3.18 | 80 | 0.002**
## RSA_alone | bgm_avg | -0.25 | [-0.44, -0.03] | -2.31 | 80 | 0.024*
## RSA_tog | bgc_avg | -0.21 | [-0.39, -0.01] | -2.11 | 100 | 0.037*
## RSA_tog | bgm_avg | -0.23 | [-0.41, -0.04] | -2.38 | 100 | 0.019*
##
## p-value adjustment method: none
## Observations: 82-102data <- data %>%
mutate(bgc_avg = rowMeans(cbind(bgc_baseline_n1_1_28, bgc_baseline_n2_1_28), na.rm = TRUE),# Calculate the average for bgm, ignoring NA values
bgm_avg = rowMeans(cbind(bgm_baseline_n1_1_28, bgm_baseline_n2_1_28), na.rm = TRUE))
library(ggplot2)
describe_distribution(data$bgc_avg)## Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## ------------------------------------------------------------------------
## 3.69 | 1.57 | 3 | [1.00, 6.00] | -0.44 | -1.21 | 117 | 25describe_distribution(data$bgm_avg)## Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## ------------------------------------------------------------------------
## 3.56 | 1.53 | 3 | [1.00, 6.00] | -0.19 | -1.16 | 117 | 25p1 <- data %>%
ggplot(aes(bgc_avg, RSA_alone)) +
geom_point(size = 4, colour = "#0072B2")+
geom_smooth(method = "lm", size = 1, color = "black", alpha = 0.2) + theme_modern() +
labs(
x = "Predominant Baseline State (r = -.33)",
y = "Infant Solo Baseline RSA"
) + scale_y_continuous(breaks = seq(1, 6, 0.5), expand = expansion(mult = 0.05)) + # More detailed RSA scale
scale_x_continuous(breaks = scales::pretty_breaks(n = 5))
p2 <- data %>%
ggplot(aes(bgm_avg, RSA_tog)) +
geom_point(size = 4, colour = "maroon")+
geom_smooth(method = "lm", size = 1, color = "black", alpha = 0.2) + theme_modern() +
labs(
x = "Predominant Baseline State (r = -.23)",
y = "Mom Infant Joint RSA"
) + scale_y_continuous(breaks = seq(1, 6, 0.5), expand = expansion(mult = 0.05)) + # More detailed RSA scale
scale_x_continuous(breaks = scales::pretty_breaks(n = 5))
see::plots(p2,p1, n_columns = 1, title = "Associations between baseline behavioral indicators & Infant RSA", subtitle = "Higher scores denote more arousal")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
ggsave("RSAArousal.png", width = 4.5, height = 7, unit = "in", dpi = 300, bg = 'white')## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'# Assign states based on ranges of bgc_avg values
data <- data %>%
mutate(bgc_state = case_when(
bgc_avg >= 0.5 & bgc_avg < 1.5 ~ "State 1: Eyes closed, regular breathing, no activity",
bgc_avg >= 1.5 & bgc_avg < 2.5 ~ "State 2: Eyes closed, irregular respiration, small movements",
bgc_avg >= 2.5 & bgc_avg < 3.5 ~ "State 3: Drowsy; minimal activity",
bgc_avg >= 3.5 & bgc_avg < 4.5 ~ "State 4: Alert, orienting to mother or object",
bgc_avg >= 4.5 & bgc_avg < 5.5 ~ "State 5: Medium motor activity, brief fussiness",
bgc_avg >= 5.5 ~ "State 6: Crying; high motor activity",
TRUE ~ NA_character_ # Handle cases that don't fit
))
# Assign states for BGM similarly
data <- data %>%
mutate(bgm_state = case_when(
bgm_avg >= 0.5 & bgm_avg < 1.5 ~ "State 1: Eyes closed, regular breathing, no activity",
bgm_avg >= 1.5 & bgm_avg < 2.5 ~ "State 2: Eyes closed, irregular respiration, small movements",
bgm_avg >= 2.5 & bgm_avg < 3.5 ~ "State 3: Drowsy; minimal activity",
bgm_avg >= 3.5 & bgm_avg < 4.5 ~ "State 4: Alert, orienting to mother or object",
bgm_avg >= 4.5 & bgm_avg < 5.5 ~ "State 5: Medium motor activity, brief fussiness",
bgm_avg >= 5.5 ~ "State 6: Crying; high motor activity",
TRUE ~ NA_character_
))
# Step 1: Filter the data to ensure complete cases for RSA and bgc_state
complete_data <- data %>% filter(!is.na(RSA_alone) & !is.na(bgc_state))
# Step 2: Regress RSA on state using the filtered data
rsa_state_model <- lm(RSA_alone ~ bgc_avg, data = complete_data)
# Step 3: Extract residuals
complete_data$rsa_residual_alone <- resid(rsa_state_model)
# Step 4: Merge the residuals back into the original dataset
# Add the residuals back to the full dataset, filling with NA where data was missing
data <- data %>%
left_join(complete_data %>% select(hhid_int, rsa_residual_alone), by = "hhid_int") # Assuming hhid_int is a unique identifier
library(stringr)
# Shorten the labels for better display in the legend
wrapped_labels <- str_wrap(c(
"State 1: Eyes closed, regular breathing, no activity",
"State 2: Eyes closed, irregular respiration, small movements",
"State 3: Drowsy; minimal activity",
"State 4: Alert, orienting to mother or object",
"State 5: Medium motor activity, brief fussiness",
"State 6: Crying; high motor activity"
), width = 25)
# Define colors for each state
state_colors <- c(
"State 1: Eyes closed, regular breathing, no activity" = "#a6cee3",
"State 2: Eyes closed, irregular respiration, small movements" = "#1f78b4",
"State 3: Drowsy; minimal activity" = "#b2df8a",
"State 4: Alert, orienting to mother or object" = "#33a02c",
"State 5: Medium motor activity, brief fussiness" = "#fb9a99",
"State 6: Crying; high motor activity" = "#e31a1c"
)
# Set factor levels to ensure consistency in the ordering
all_states <- names(state_colors)
# Apply factor levels to both bgc_state and bgm_state
data$bgc_state <- factor(data$bgc_state, levels = all_states)
data$bgm_state <- factor(data$bgm_state, levels = all_states)
# Reshape the data for the stacked bar plot (long format)
stacked_data <- data %>%
select(bgc_state, bgm_state) %>%
pivot_longer(cols = c(bgc_state, bgm_state), names_to = "Measurement", values_to = "State")
# Remove rows with NA in the 'State' column
stacked_data_clean <- stacked_data %>%
filter(!is.na(State))
# Calculate counts and proportions for each state in bgc and bgm
stacked_data_clean <- stacked_data_clean %>%
group_by(Measurement, State) %>%
summarise(count = n(), .groups = 'drop') %>%
group_by(Measurement) %>%
mutate(percentage = count / sum(count) * 100) # Calculate percentage for each state
# Create the stacked bar plot with percentage labels and custom colors
stacked_plot_clean <- ggplot(stacked_data_clean, aes(x = Measurement, y = count, fill = State)) +
geom_bar(stat = "identity", position = "stack", color = "black", alpha = 0.8) + # Stacked bars with black outline
geom_text(aes(label = sprintf("%.1f%%", percentage)), # Add percentage labels
position = position_stack(vjust = 0.5), size = 3) + # Position text in the middle of each stack
scale_fill_manual(values = state_colors, labels = wrapped_labels) + # Use custom colors and wrapped labels
labs(title = "Stacked Bar Plot of BGC and BGM States with Percentages",
x = "Measurement",
y = "Count",
fill = "State") + # Label the legend
scale_x_discrete(labels = c("bgc_state" = "State - Alone", "bgm_state" = "State - Joint baseline")) + # Relabel x-axis
theme_minimal() + # Minimal theme for a clean look
theme(
plot.title = element_text(size = 14, face = "bold"), # Customize the title
axis.title.x = element_text(size = 12), # Customize x-axis title
axis.title.y = element_text(size = 12), # Customize y-axis title
legend.position = "right", # Adjust legend position
legend.text = element_text(size = 8) # Adjust legend text size
)
# Display the plot
print(stacked_plot_clean)
# Reshape the data for the stacked bar plot (long format)
stacked_data <- data %>%
select(bgc_state, bgm_state, date_category) %>% # Include date_category in the data
pivot_longer(cols = c(bgc_state, bgm_state), names_to = "Measurement", values_to = "State")
# Remove rows with NA in the 'State' column
stacked_data_clean <- stacked_data %>%
filter(!is.na(State))
# Calculate counts and proportions for each state in bgc and bgm
stacked_data_clean <- stacked_data_clean %>%
group_by(Measurement, State, date_category) %>% # Group by date_category for faceting
summarise(count = n(), .groups = 'drop') %>%
group_by(Measurement, date_category) %>%
mutate(percentage = count / sum(count) * 100) # Calculate percentage for each state within date_category
# Create the stacked bar plot with percentage labels and custom colors, faceted by date_category
stacked_plot_clean <- ggplot(stacked_data_clean, aes(x = Measurement, y = count, fill = State)) +
geom_bar(stat = "identity", position = "stack", color = "black", alpha = 0.8) + # Stacked bars with black outline
geom_text(aes(label = sprintf("%.1f%%", percentage)), # Add percentage labels
position = position_stack(vjust = 0.5), size = 3) + # Position text in the middle of each stack
scale_fill_manual(values = state_colors, labels = wrapped_labels) + # Use custom colors and wrapped labels
labs(title = "Changes in arousal leves after we adjusted the protocol",
x = "Measurement",
y = "Count",
fill = "State") + # Label the legend
scale_x_discrete(labels = c("bgc_state" = "State - Alone", "bgm_state" = "State - Joint baseline")) + # Relabel x-axis
theme_minimal() + # Minimal theme for a clean look
theme(
plot.title = element_text(size = 14, face = "bold"), # Customize the title
axis.title.x = element_text(size = 12), # Customize x-axis title
axis.title.y = element_text(size = 12), # Customize y-axis title
legend.position = "right", # Adjust legend position
legend.text = element_text(size = 8),
strip.text = element_text(size = 14)# Adjust legend text size
) +
facet_wrap(~ date_category) # Create panels for each date_category
# Display the plot
print(stacked_plot_clean)
ggsave("Arousal.png", width = 7, height = 6, unit = "in", dpi = 300, bg = 'white')
table(data$bgm_qus_face_1_28)##
## 0 1
## 105 14lessR::tt_brief(RSA_tog ~ bgm_qus_face_1_28, data = data)##
## Compare RSA_tog across bgm_qus_face_1_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: bgm_qus_face_1_28,
##
##
## --- Describe ---
##
## RSA_tog for bgm_qus_face_1_28 0: n.miss = 14, n = 91, mean = 2.661441195, sd = 1.037933263
## RSA_tog for bgm_qus_face_1_28 1: n.miss = 1, n = 13, mean = 1.963242510, sd = 0.750062987
##
## Mean Difference of RSA_tog: 0.698198686
## Weighted Average Standard Deviation: 1.008340829
## Standardized Mean Difference of RSA_tog: 0.692423302
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.298972780
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.335, df = 102, p-value = 0.021
##
## Margin of Error for 95% Confidence Level: 0.593011092
## 95% Confidence Interval for Mean Difference: 0.105187594 to 1.291209778
#table(data$bgm_qus_kissing_1_28)
#lessR::tt_brief(RSA_tog ~ bgm_qus_kissing_1_28, data = data)
table(data$bgm_qus_bfeed_1_28)##
## 0 1
## 113 6lessR::tt_brief(RSA_tog ~ bgm_qus_bfeed_1_28, data = data)##
## Compare RSA_tog across bgm_qus_bfeed_1_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: bgm_qus_bfeed_1_28,
##
##
## --- Describe ---
##
## RSA_tog for bgm_qus_bfeed_1_28 0: n.miss = 15, n = 98, mean = 2.613422841, sd = 1.035725121
## RSA_tog for bgm_qus_bfeed_1_28 1: n.miss = 0, n = 6, mean = 1.932977172, sd = 0.718538334
##
## Mean Difference of RSA_tog: 0.680445669
## Weighted Average Standard Deviation: 1.022472780
## Standardized Mean Difference of RSA_tog: 0.665490253
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.430011194
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.582, df = 102, p-value = 0.117
##
## Margin of Error for 95% Confidence Level: 0.852925165
## 95% Confidence Interval for Mean Difference: -0.172479496 to 1.533370834
table(data$bgm_qus_rocking_1_28)##
## 0 1
## 57 62lessR::tt_brief(RSA_tog ~ bgm_qus_rocking_1_28, data = data)##
## Compare RSA_tog across bgm_qus_rocking_1_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: bgm_qus_rocking_1_28,
##
##
## --- Describe ---
##
## RSA_tog for bgm_qus_rocking_1_28 0: n.miss = 4, n = 53, mean = 2.612914237, sd = 1.083390440
## RSA_tog for bgm_qus_rocking_1_28 1: n.miss = 11, n = 51, mean = 2.533898958, sd = 0.980379982
##
## Mean Difference of RSA_tog: 0.079015279
## Weighted Average Standard Deviation: 1.034177979
## Standardized Mean Difference of RSA_tog: 0.076403946
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.202856502
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.390, df = 102, p-value = 0.698
##
## Margin of Error for 95% Confidence Level: 0.402364910
## 95% Confidence Interval for Mean Difference: -0.323349632 to 0.481380189
table(data$bgm_qus_stroking_1_28)##
## 0 1
## 107 12lessR::tt_brief(RSA_tog ~ bgm_qus_stroking_1_28, data = data)##
## Compare RSA_tog across bgm_qus_stroking_1_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: bgm_qus_stroking_1_28,
##
##
## --- Describe ---
##
## RSA_tog for bgm_qus_stroking_1_28 0: n.miss = 10, n = 97, mean = 2.638931318, sd = 1.018606879
## RSA_tog for bgm_qus_stroking_1_28 1: n.miss = 5, n = 7, mean = 1.676709078, sd = 0.774929582
##
## Mean Difference of RSA_tog: 0.962222241
## Weighted Average Standard Deviation: 1.005908294
## Standardized Mean Difference of RSA_tog: 0.956570540
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.393677117
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.444, df = 102, p-value = 0.016
##
## Margin of Error for 95% Confidence Level: 0.780856695
## 95% Confidence Interval for Mean Difference: 0.181365545 to 1.743078936
table(data$bgm_qus_talking_1_28)##
## 0 1
## 108 11lessR::tt_brief(RSA_tog ~ bgm_qus_talking_1_28, data = data)##
## Compare RSA_tog across bgm_qus_talking_1_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: bgm_qus_talking_1_28,
##
##
## --- Describe ---
##
## RSA_tog for bgm_qus_talking_1_28 0: n.miss = 14, n = 94, mean = 2.619762064, sd = 1.038754729
## RSA_tog for bgm_qus_talking_1_28 1: n.miss = 1, n = 10, mean = 2.145566740, sd = 0.873897265
##
## Mean Difference of RSA_tog: 0.474195324
## Weighted Average Standard Deviation: 1.025275318
## Standardized Mean Difference of RSA_tog: 0.462505354
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.341030519
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.390, df = 102, p-value = 0.167
##
## Margin of Error for 95% Confidence Level: 0.676432417
## 95% Confidence Interval for Mean Difference: -0.202237093 to 1.150627741
ANOVA(RSA_alone ~ fussiness_1_28, data)
##
## BACKGROUND
##
## Response Variable: RSA_alone
##
## Factor Variable: fussiness_1_28
## Levels: 1 2 3
##
## Number of cases (rows) of data: 142
## Number of cases retained for analysis: 82
##
##
## DESCRIPTIVE STATISTICS
##
## n mean sd min max
## 1 4 2.44191578 1.40934657 1.44473560 4.51263513
## 2 51 2.45518236 1.14651828 0.49254722 5.75527589
## 3 27 2.90212801 1.30302229 0.42643528 5.17019126
##
## Grand Mean: 2.601700244
##
##
## ANOVA
##
## df Sum Sq Mean Sq F-value p-value
## fussiness_1_28 2 3.63390099 1.81695050 1.23923782 0.2952
## Residuals 79 115.82852571 1.46618387
##
## R Squared: 0.030
## R Sq Adjusted: 0.016
## Omega Squared: 0.006
##
##
## Cohen's f: 0.076
##
##
## TUKEY MULTIPLE COMPARISONS OF MEANS
##
## Family-wise Confidence Level: 0.95
## --------------------------------------------------
## diff lwr upr p adj
## 2-1 0.01326658 -1.48855217 1.51508534 0.99975456
## 3-1 0.46021223 -1.08939000 2.00981445 0.75866586
## 3-2 0.44694565 -0.24143966 1.13533096 0.27306684
##
##
## RESIDUALS
##
## Fitted Values, Residuals, Standardized Residuals
## [sorted by Standardized Residuals, ignoring + or - sign]
## [res_rows = 20, out of 82 cases (rows) of data, or res_rows="all"]
## --------------------------------------------------------------
## fussiness_1_28 RSA_alone fitted residual z-resid
## 95 2 5.7552759 2.4551824 3.3000935 2.7525303
## 123 2 5.0916757 2.4551824 2.6364934 2.1990371
## 142 3 0.4264353 2.9021280 -2.4756927 -2.0835203
## 10 1 4.5126351 2.4419158 2.0707193 1.9746785
## 6 3 5.1701913 2.9021280 2.2680632 1.9087812
## 32 3 5.1095165 2.9021280 2.2073885 1.8577179
## 99 2 4.6399367 2.4551824 2.1847543 1.8222522
## 8 2 4.6374029 2.4551824 2.1822205 1.8201388
## 77 3 5.0627174 2.9021280 2.1605894 1.8183322
## 29 3 0.7697158 2.9021280 -2.1324122 -1.7946185
## 13 2 0.4925472 2.4551824 -1.9626351 -1.6369878
## 44 3 1.1185077 2.9021280 -1.7836203 -1.5010785
## 18 3 4.6050791 2.9021280 1.7029511 1.4331880
## 91 2 0.8714660 2.4551824 -1.5837164 -1.3209405
## 121 3 1.3439190 2.9021280 -1.5582090 -1.3113744
## 87 2 4.0016286 2.4551824 1.5464462 1.2898544
## 15 2 0.9248220 2.4551824 -1.5303603 -1.2764375
## 82 2 0.9878737 2.4551824 -1.4673086 -1.2238476
## 76 2 1.0075809 2.4551824 -1.4476015 -1.2074104
## 38 2 1.0817164 2.4551824 -1.3734660 -1.1455757
##
## ----------------------------------------
## Plot 1: 95% family-wise confidence level
## Plot 2: Scatterplot with Cell Means
## ----------------------------------------ANOVA(RSA_tog ~ fussiness_1_28, data)
##
## BACKGROUND
##
## Response Variable: RSA_tog
##
## Factor Variable: fussiness_1_28
## Levels: 1 2 3
##
## Number of cases (rows) of data: 142
## Number of cases retained for analysis: 104
##
##
## DESCRIPTIVE STATISTICS
##
## n mean sd min max
## 1 5 2.915267020 1.495003611 1.291251789 4.516449215
## 2 74 2.554255135 0.941502398 0.626715921 4.637900423
## 3 25 2.564883452 1.207462709 0.089632828 4.568989290
##
## Grand Mean: 2.5741663597
##
##
## ANOVA
##
## df Sum Sq Mean Sq F-value p-value
## fussiness_1_28 2 0.613240418 0.306620209 0.285056173 0.7526
## Residuals 101 108.640485729 1.075648374
##
## R Squared: 0.006
## R Sq Adjusted: -0.009
## Omega Squared: -0.014
##
##
## TUKEY MULTIPLE COMPARISONS OF MEANS
##
## Family-wise Confidence Level: 0.95
## -------------------------------------------------------
## diff lwr upr p adj
## 2-1 -0.361011884 -1.500993608 0.778969839 0.732329001
## 3-1 -0.350383568 -1.559006488 0.858239353 0.770090145
## 3-2 0.010628317 -0.560083596 0.581340230 0.998918654
##
##
## RESIDUALS
##
## Fitted Values, Residuals, Standardized Residuals
## [sorted by Standardized Residuals, ignoring + or - sign]
## [res_rows = 20, out of 104 cases (rows) of data, or res_rows="all"]
## ------------------------------------------------------------------
## fussiness_1_28 RSA_tog fitted residual z-resid
## 142 3 0.08963283 2.56488345 -2.47525062 -2.43583799
## 105 3 0.44957853 2.56488345 -2.11530492 -2.08162359
## 56 2 4.63790042 2.55425514 2.08364529 2.02275394
## 6 3 4.56898929 2.56488345 2.00410584 1.97219509
## 30 2 4.58077739 2.55425514 2.02652225 1.96730024
## 66 2 0.62671592 2.55425514 -1.92753921 -1.87120983
## 4 2 0.73155758 2.55425514 -1.82269756 -1.76943201
## 11 1 1.29125179 2.91526702 -1.62401523 -1.75069278
## 10 1 4.51644921 2.91526702 1.60118220 1.72607871
## 129 1 4.47634939 2.91526702 1.56108237 1.68285100
## 111 3 4.26270743 2.56488345 1.69782397 1.67079006
## 45 3 4.24219180 2.56488345 1.67730835 1.65060110
## 121 3 0.94559980 2.56488345 -1.61928366 -1.59350031
## 14 2 0.93297024 2.55425514 -1.62128489 -1.57390532
## 74 2 4.09886252 2.55425514 1.54460738 1.49946859
## 76 2 1.01887036 2.55425514 -1.53538478 -1.49051550
## 123 2 4.04540144 2.55425514 1.49114631 1.44756983
## 81 3 1.09421656 2.56488345 -1.47066690 -1.44724993
## 75 2 1.12220879 2.55425514 -1.43204635 -1.39019698
## 100 2 3.95197414 2.55425514 1.39771901 1.35687281
##
## ----------------------------------------
## Plot 1: 95% family-wise confidence level
## Plot 2: Scatterplot with Cell Means
## ----------------------------------------completedat <- data %>% filter(!is.na(RSA_alone) & !is.na(RSA_tog))
data %>%
ggplot(aes(RSA_alone, RSA_tog)) +
geom_point(size = 4, colour = "maroon")+
geom_smooth(method = "lm", size = 1, color = "black", alpha = 0.2) + theme_modern() +
labs(
x = "RSA Alone",
y = "RSA with Mom"
)## `geom_smooth()` using formula = 'y ~ x'
RSA quality predictors
% of children with full baseline with mom % of children with partial baseline with mom % of children with full baseline alone % of children with partial baseline alone % of children with unusable data
## [1] 142 119## Variable | Level | n_Obs | percentage_Obs
## ---------------------------------------------
## bgc_a_b_comp | 0 | 59 | 41.55
## bgc_a_b_comp | 1 | 83 | 58.45
## bgc_a_b_par | 0 | 135 | 95.07
## bgc_a_b_par | 1 | 7 | 4.93
## bgc_a_b_un | 0 | 136 | 95.77
## bgc_a_b_un | 1 | 6 | 4.23
## bgc_a_b_nq | 0 | 111 | 78.17
## bgc_a_b_nq | 1 | 31 | 21.83
## bgc_a_b_na | 0 | 130 | 91.55
## bgc_a_b_na | 1 | 12 | 8.45## Variable | Level | n_Obs | percentage_Obs
## ---------------------------------------------
## bgm_t_b_comp | 0 | 37 | 26.06
## bgm_t_b_comp | 1 | 105 | 73.94
## bgm_t_b_par | 0 | 125 | 88.03
## bgm_t_b_par | 1 | 17 | 11.97
## bgm_t_b_un | 0 | 130 | 91.55
## bgm_t_b_un | 1 | 12 | 8.45
## bgm_t_b_nq | 0 | 141 | 99.30
## bgm_t_b_nq | 1 | 1 | 0.70
## bgm_t_b_na | 0 | 136 | 95.77
## bgm_t_b_na | 1 | 6 | 4.23## Variable | Level | n_Obs | percentage_Obs
## --------------------------------------------------
## together_usable | 0 | 12 | 8.45
## together_usable | 1 | 122 | 85.92
## together_usable | 99 | 5 | 3.52
## together_usable | missing | 3 | 2.11
## alone_usable | 0 | 6 | 4.23
## alone_usable | 1 | 90 | 63.38
## alone_usable | 99 | 12 | 8.45
## alone_usable | missing | 34 | 23.94## Variable | Level | n_Obs | percentage_Obs
## ----------------------------------------------------
## together_usable | 0 | 12 | 8.51
## together_usable | 1 | 122 | 86.52
## together_usable | 99 | 5 | 3.55
## together_usable | missing | 2 | 1.42
## together_usable_b | 0 | 17 | 12.06
## together_usable_b | 1 | 122 | 86.52
## together_usable_b | missing | 2 | 1.42## Variable | Level | n_Obs | percentage_Obs
## -------------------------------------------------
## alone_usable | 0 | 6 | 5.41
## alone_usable | 1 | 90 | 81.08
## alone_usable | 99 | 12 | 10.81
## alone_usable | missing | 3 | 2.70
## alone_usable_b | 0 | 18 | 16.22
## alone_usable_b | 1 | 90 | 81.08
## alone_usable_b | missing | 3 | 2.70## # A tibble: 3 × 2
## `is.na(bgc_avg) & alone_usable == 99` n
## <lgl> <int>
## 1 FALSE 125
## 2 TRUE 6
## 3 NA 11## # A tibble: 2 × 2
## `!is.na(RSA_tog) & !is.na(RSA_alone)` n
## <lgl> <int>
## 1 FALSE 74
## 2 TRUE 68## # A tibble: 2 × 2
## `!is.na(RSA_tog) & is.na(RSA_alone)` n
## <lgl> <int>
## 1 FALSE 105
## 2 TRUE 37## # A tibble: 2 × 2
## `is.na(RSA_tog) & !is.na(RSA_alone)` n
## <lgl> <int>
## 1 FALSE 127
## 2 TRUE 15## # A tibble: 142 × 2
## hhid_int n
## <dbl> <int>
## 1 45 1
## 2 171 1
## 3 176 1
## 4 183 1
## 5 202 1
## 6 204 1
## 7 213 1
## 8 214 1
## 9 235 1
## 10 257 1
## # ℹ 132 more rows
##
## BACKGROUND
##
## Response Variable: bgc_avg
##
## Factor Variable: alone_usable
## Levels: 0 1 99
##
## Number of cases (rows) of data: 142
## Number of cases retained for analysis: 94
##
##
## DESCRIPTIVE STATISTICS
##
## n mean sd min max
## 0 3 5.50 0.50 5.00 6.00
## 1 85 3.76 1.57 1.00 6.00
## 99 6 4.67 0.52 4.00 5.00
##
## Grand Mean: 3.878
##
##
## ANOVA
##
## df Sum Sq Mean Sq F-value p-value
## alone_usable 2 12.72 6.36 2.76 0.0686
## Residuals 91 209.63 2.30
##
## R Squared: 0.057
## R Sq Adjusted: 0.044
## Omega Squared: 0.036
##
##
## Cohen's f: 0.194
##
##
## TUKEY MULTIPLE COMPARISONS OF MEANS
##
## Family-wise Confidence Level: 0.95
## -----------------------------
## diff lwr upr p adj
## 1-0 -1.74 -3.86 0.39 0.13
## 99-0 -0.83 -3.39 1.72 0.72
## 99-1 0.90 -0.63 2.43 0.34
##
##
## RESIDUALS
##
## Fitted Values, Residuals, Standardized Residuals
## [sorted by Standardized Residuals, ignoring + or - sign]
## [res_rows = 20, out of 94 cases (rows) of data, or res_rows="all"]
## --------------------------------------------------
## alone_usable bgc_avg fitted residual z-resid
## 10 1 1.00 3.76 -2.76 -1.83
## 87 1 1.00 3.76 -2.76 -1.83
## 90 1 1.00 3.76 -2.76 -1.83
## 94 1 1.00 3.76 -2.76 -1.83
## 99 1 1.00 3.76 -2.76 -1.83
## 100 1 1.00 3.76 -2.76 -1.83
## 109 1 1.00 3.76 -2.76 -1.83
## 118 1 1.00 3.76 -2.76 -1.83
## 125 1 1.00 3.76 -2.76 -1.83
## 17 1 1.50 3.76 -2.26 -1.50
## 43 1 1.50 3.76 -2.26 -1.50
## 115 1 1.50 3.76 -2.26 -1.50
## 123 1 1.50 3.76 -2.26 -1.50
## 9 1 6.00 3.76 2.24 1.48
## 13 1 6.00 3.76 2.24 1.48
## 38 1 6.00 3.76 2.24 1.48
## 6 1 6.00 3.76 2.24 1.48
## 5 1 6.00 3.76 2.24 1.48
## 32 1 2.00 3.76 -1.76 -1.17
## 45 1 2.00 3.76 -1.76 -1.17
##
## ----------------------------------------
## Plot 1: 95% family-wise confidence level
## Plot 2: Scatterplot with Cell Means
## ----------------------------------------
##
## BACKGROUND
##
## Response Variable: bgm_avg
##
## Factor Variable: together_usable
## Levels: 0 1 99
##
## Number of cases (rows) of data: 142
## Number of cases retained for analysis: 116
##
##
## DESCRIPTIVE STATISTICS
##
## n mean sd min max
## 0 5 4.70 0.84 4.00 6.00
## 1 107 3.44 1.50 1.00 6.00
## 99 4 5.00 2.00 2.00 6.00
##
## Grand Mean: 3.547
##
##
## ANOVA
##
## df Sum Sq Mean Sq F-value p-value
## together_usable 2 16.33 8.17 3.64 0.0294
## Residuals 113 253.66 2.24
##
## R Squared: 0.060
## R Sq Adjusted: 0.047
## Omega Squared: 0.044
##
##
## Cohen's f: 0.213
##
##
## TUKEY MULTIPLE COMPARISONS OF MEANS
##
## Family-wise Confidence Level: 0.95
## -----------------------------
## diff lwr upr p adj
## 1-0 -1.26 -2.89 0.37 0.16
## 99-0 0.30 -2.09 2.69 0.95
## 99-1 1.56 -0.25 3.37 0.11
##
##
## RESIDUALS
##
## Fitted Values, Residuals, Standardized Residuals
## [sorted by Standardized Residuals, ignoring + or - sign]
## [res_rows = 20, out of 116 cases (rows) of data, or res_rows="all"]
## -----------------------------------------------------
## together_usable bgm_avg fitted residual z-resid
## 91 99 2.00 5.00 -3.00 -2.31
## 6 1 6.00 3.44 2.56 1.72
## 9 1 6.00 3.44 2.56 1.72
## 16 1 6.00 3.44 2.56 1.72
## 38 1 6.00 3.44 2.56 1.72
## 17 1 1.00 3.44 -2.44 -1.64
## 54 1 1.00 3.44 -2.44 -1.64
## 74 1 1.00 3.44 -2.44 -1.64
## 87 1 1.00 3.44 -2.44 -1.64
## 90 1 1.00 3.44 -2.44 -1.64
## 94 1 1.00 3.44 -2.44 -1.64
## 100 1 1.00 3.44 -2.44 -1.64
## 105 1 1.00 3.44 -2.44 -1.64
## 109 1 1.00 3.44 -2.44 -1.64
## 112 1 1.00 3.44 -2.44 -1.64
## 118 1 1.00 3.44 -2.44 -1.64
## 124 1 1.00 3.44 -2.44 -1.64
## 27 1 5.50 3.44 2.06 1.38
## 37 1 5.50 3.44 2.06 1.38
## 60 1 5.50 3.44 2.06 1.38
##
## ----------------------------------------
## Plot 1: 95% family-wise confidence level
## Plot 2: Scatterplot with Cell Means
## ----------------------------------------##
## Compare bgc_avg across alone_usable_b with levels 0 and 1
## Response Variable: bgc_avg, bgc_avg
## Grouping Variable: alone_usable_b,
##
##
## --- Describe ---
##
## bgc_avg for alone_usable_b 0: n.miss = 9, n = 9, mean = 4.944, sd = 0.635
## bgc_avg for alone_usable_b 1: n.miss = 5, n = 85, mean = 3.765, sd = 1.573
##
## Mean Difference of bgc_avg: 1.180
## Weighted Average Standard Deviation: 1.514
## Standardized Mean Difference of bgc_avg: 0.779
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.531
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.222, df = 92, p-value = 0.029
##
## Margin of Error for 95% Confidence Level: 1.054
## 95% Confidence Interval for Mean Difference: 0.125 to 2.234
##
## Compare bgm_avg across together_usable_b with levels 0 and 1
## Response Variable: bgm_avg, bgm_avg
## Grouping Variable: together_usable_b,
##
##
## --- Describe ---
##
## bgm_avg for together_usable_b 0: n.miss = 8, n = 9, mean = 4.833, sd = 1.369
## bgm_avg for together_usable_b 1: n.miss = 15, n = 107, mean = 3.439, sd = 1.501
##
## Mean Difference of bgm_avg: 1.394
## Weighted Average Standard Deviation: 1.492
## Standardized Mean Difference of bgm_avg: 0.934
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.981
## Standard Error of Mean Difference: SE = 0.518
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.692, df = 114, p-value = 0.008
##
## Margin of Error for 95% Confidence Level: 1.026
## 95% Confidence Interval for Mean Difference: 0.368 to 2.420
##
## Compare bgc_avg across bgc_a_b_par with levels 1 and 0
## Response Variable: bgc_avg, bgc_avg
## Grouping Variable: bgc_a_b_par,
##
##
## --- Describe ---
##
## bgc_avg for bgc_a_b_par 1: n.miss = 4, n = 3, mean = 4.167, sd = 1.258
## bgc_avg for bgc_a_b_par 0: n.miss = 13, n = 91, mean = 3.868, sd = 1.560
##
## Mean Difference of bgc_avg: 0.299
## Weighted Average Standard Deviation: 1.554
## Standardized Mean Difference of bgc_avg: 0.192
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.912
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.327, df = 92, p-value = 0.744
##
## Margin of Error for 95% Confidence Level: 1.811
## 95% Confidence Interval for Mean Difference: -1.512 to 2.109
##
## Compare bgm_avg across date_category with levels October 2023 and January 2024
## Response Variable: bgm_avg, bgm_avg
## Grouping Variable: date_category,
##
##
## --- Describe ---
##
## bgm_avg for date_category October 2023: n.miss = 10, n = 45, mean = 4.122, sd = 1.474
## bgm_avg for date_category January 2024: n.miss = 7, n = 49, mean = 3.224, sd = 1.479
##
## Mean Difference of bgm_avg: 0.898
## Weighted Average Standard Deviation: 1.477
## Standardized Mean Difference of bgm_avg: 0.608
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.305
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.944, df = 92, p-value = 0.004
##
## Margin of Error for 95% Confidence Level: 0.606
## 95% Confidence Interval for Mean Difference: 0.292 to 1.503
##
## Compare bgc_avg across date_category with levels October 2023 and January 2024
## Response Variable: bgc_avg, bgc_avg
## Grouping Variable: date_category,
##
##
## --- Describe ---
##
## bgc_avg for date_category October 2023: n.miss = 13, n = 59, mean = 4.017, sd = 1.600
## bgc_avg for date_category January 2024: n.miss = 12, n = 58, mean = 3.362, sd = 1.480
##
## Mean Difference of bgc_avg: 0.655
## Weighted Average Standard Deviation: 1.542
## Standardized Mean Difference of bgc_avg: 0.425
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.981
## Standard Error of Mean Difference: SE = 0.285
##
## Hypothesis Test of 0 Mean Diff: t-value = 2.297, df = 115, p-value = 0.023
##
## Margin of Error for 95% Confidence Level: 0.565
## 95% Confidence Interval for Mean Difference: 0.090 to 1.220
##
## Compare RSA_tog across date_category with levels January 2024 and October 2023
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: date_category,
##
##
## --- Describe ---
##
## RSA_tog for date_category January 2024: n.miss = 16, n = 54, mean = 2.60273137, sd = 1.02962524
## RSA_tog for date_category October 2023: n.miss = 21, n = 51, mean = 2.56138744, sd = 1.03738174
##
## Mean Difference of RSA_tog: 0.04134393
## Weighted Average Standard Deviation: 1.03339780
## Standardized Mean Difference of RSA_tog: 0.04000776
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.20178097
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.205, df = 103, p-value = 0.838
##
## Margin of Error for 95% Confidence Level: 0.40018497
## 95% Confidence Interval for Mean Difference: -0.35884104 to 0.44152891
##
## Compare RSA_alone across date_category with levels January 2024 and October 2023
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: date_category,
##
##
## --- Describe ---
##
## RSA_alone for date_category January 2024: n.miss = 23, n = 47, mean = 2.69033837, sd = 1.16762668
## RSA_alone for date_category October 2023: n.miss = 36, n = 36, mean = 2.50385687, sd = 1.26996722
##
## Mean Difference of RSA_alone: 0.18648151
## Weighted Average Standard Deviation: 1.21290785
## Standardized Mean Difference of RSA_alone: 0.15374746
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.26863752
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.694, df = 81, p-value = 0.490
##
## Margin of Error for 95% Confidence Level: 0.53450440
## 95% Confidence Interval for Mean Difference: -0.34802290 to 0.72098591
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 1.0078, df = 1, p-value = 0.3154
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.0038317, df = 1, p-value = 0.9506
## >>> Suggestions
## Plot(together_usable_b, date_category) # bubble plot
## BarChart(together_usable_b, by=date_category, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## date_category 0 1 Sum
## October 2023 11 59 70
## January 2024 6 63 69
## Sum 17 122 139
##
## Cramer's V (phi): 0.107
##
## Chi-square Test of Independence:
## Chisq = 1.595, df = 1, p-value = 0.207
##
## Cell Proportions within Each Column
## -----------------------------------
##
## together_usable_b
## date_category 0 1
## October 2023 0.647 0.484
## January 2024 0.353 0.516
## Sum 1.000 1.000
## >>> Suggestions
## Plot(alone_usable_b, date_category) # bubble plot
## BarChart(alone_usable_b, by=date_category, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## date_category 0 1 Sum
## October 2023 12 41 53
## January 2024 6 49 55
## Sum 18 90 108
##
## Cramer's V (phi): 0.157
##
## Chi-square Test of Independence:
## Chisq = 2.675, df = 1, p-value = 0.102
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## date_category 0 1
## October 2023 0.667 0.456
## January 2024 0.333 0.544
## Sum 1.000 1.000
## >>> Suggestions
## Plot(restype, together_usable_b) # bubble plot
## BarChart(restype, by=together_usable_b, horiz=TRUE) # horizontal bar chart
## BarChart(restype, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## restype
## together_usable_b 1 2 Sum
## 0 8 1 9
## 1 85 25 110
## Sum 93 26 119
##
## Cramer's V (phi): 0.074
##
## Chi-square Test of Independence:
## Chisq = 0.657, df = 1, p-value = 0.417
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## restype
## together_usable_b 1 2
## 0 0.086 0.038
## 1 0.914 0.962
## Sum 1.000 1.000
## >>> Suggestions
## Plot(restype, alone_usable_b) # bubble plot
## BarChart(restype, by=alone_usable_b, horiz=TRUE) # horizontal bar chart
## BarChart(restype, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## restype
## alone_usable_b 1 2 Sum
## 0 7 4 11
## 1 70 16 86
## Sum 77 20 97
##
## Cramer's V (phi): 0.139
##
## Chi-square Test of Independence:
## Chisq = 1.879, df = 1, p-value = 0.170
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## restype
## alone_usable_b 1 2
## 0 0.091 0.200
## 1 0.909 0.800
## Sum 1.000 1.000##
## 0 1
## 105 14
Pearson's Chi-squared test with Yates' continuity correction
data: tab
X-squared = 0.00000000000000000000000000000017756, df = 1, p-value = 1
## >>> Suggestions
## Plot(together_usable_b, bgm_qus_face_1_28) # bubble plot
## BarChart(together_usable_b, by=bgm_qus_face_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## bgm_qus_face_1_28 0 1 Sum
## 0 8 96 104
## 1 1 13 14
## Sum 9 109 118
##
## Cramer's V (phi): 0.007
##
## Chi-square Test of Independence:
## Chisq = 0.005, df = 1, p-value = 0.942
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
## >>> Suggestions
## Plot(alone_usable_b, bgm_qus_face_1_28) # bubble plot
## BarChart(alone_usable_b, by=bgm_qus_face_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## bgm_qus_face_1_28 0 1 Sum
## 0 8 76 84
## 1 3 9 12
## Sum 11 85 96
##
## Cramer's V (phi): 0.161
##
## Chi-square Test of Independence:
## Chisq = 2.479, df = 1, p-value = 0.115
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## bgm_qus_face_1_28 0 1
## 0 0.727 0.894
## 1 0.273 0.106
## Sum 1.000 1.000##
## 0 1
## 113 6
## >>> Suggestions
## Plot(together_usable_b, bgm_qus_bfeed_1_28) # bubble plot
## BarChart(together_usable_b, by=bgm_qus_bfeed_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## bgm_qus_bfeed_1_28 0 1 Sum
## 0 9 103 112
## 1 0 6 6
## Sum 9 109 118
##
## Cramer's V (phi): 0.067
##
## Chi-square Test of Independence:
## Chisq = 0.522, df = 1, p-value = 0.470
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## together_usable_b
## bgm_qus_bfeed_1_28 0 1
## 0 1.000 0.945
## 1 0.000 0.055
## Sum 1.000 1.000
## >>> Suggestions
## Plot(alone_usable_b, bgm_qus_bfeed_1_28) # bubble plot
## BarChart(alone_usable_b, by=bgm_qus_bfeed_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## bgm_qus_bfeed_1_28 0 1 Sum
## 0 11 79 90
## 1 0 6 6
## Sum 11 85 96
##
## Cramer's V (phi): 0.093
##
## Chi-square Test of Independence:
## Chisq = 0.828, df = 1, p-value = 0.363
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## bgm_qus_bfeed_1_28 0 1
## 0 1.000 0.929
## 1 0.000 0.071
## Sum 1.000 1.000##
## 0 1
## 57 62
## >>> Suggestions
## Plot(together_usable_b, bgm_qus_rocking_1_28) # bubble plot
## BarChart(together_usable_b, by=bgm_qus_rocking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## bgm_qus_rocking_1_28 0 1 Sum
## 0 2 55 57
## 1 7 54 61
## Sum 9 109 118
##
## Cramer's V (phi): 0.150
##
## Chi-square Test of Independence:
## Chisq = 2.654, df = 1, p-value = 0.103
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## together_usable_b
## bgm_qus_rocking_1_28 0 1
## 0 0.222 0.505
## 1 0.778 0.495
## Sum 1.000 1.000
## >>> Suggestions
## Plot(alone_usable_b, bgm_qus_rocking_1_28) # bubble plot
## BarChart(alone_usable_b, by=bgm_qus_rocking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## bgm_qus_rocking_1_28 0 1 Sum
## 0 4 42 46
## 1 7 43 50
## Sum 11 85 96
##
## Cramer's V (phi): 0.083
##
## Chi-square Test of Independence:
## Chisq = 0.664, df = 1, p-value = 0.415
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## bgm_qus_rocking_1_28 0 1
## 0 0.364 0.494
## 1 0.636 0.506
## Sum 1.000 1.000##
## 0 1
## 107 12
## >>> Suggestions
## Plot(together_usable_b, bgm_qus_stroking_1_28) # bubble plot
## BarChart(together_usable_b, by=bgm_qus_stroking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## bgm_qus_stroking_1_28 0 1 Sum
## 0 7 99 106
## 1 2 10 12
## Sum 9 109 118
##
## Cramer's V (phi): 0.115
##
## Chi-square Test of Independence:
## Chisq = 1.549, df = 1, p-value = 0.213
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## together_usable_b
## bgm_qus_stroking_1_28 0 1
## 0 0.778 0.908
## 1 0.222 0.092
## Sum 1.000 1.000
## >>> Suggestions
## Plot(alone_usable_b, bgm_qus_stroking_1_28) # bubble plot
## BarChart(alone_usable_b, by=bgm_qus_stroking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## bgm_qus_stroking_1_28 0 1 Sum
## 0 10 75 85
## 1 1 10 11
## Sum 11 85 96
##
## Cramer's V (phi): 0.027
##
## Chi-square Test of Independence:
## Chisq = 0.069, df = 1, p-value = 0.793
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## bgm_qus_stroking_1_28 0 1
## 0 0.909 0.882
## 1 0.091 0.118
## Sum 1.000 1.000##
## 0 1
## 108 11
## >>> Suggestions
## Plot(together_usable_b, bgm_qus_talking_1_28) # bubble plot
## BarChart(together_usable_b, by=bgm_qus_talking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(together_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## together_usable_b
## bgm_qus_talking_1_28 0 1 Sum
## 0 8 99 107
## 1 1 10 11
## Sum 9 109 118
##
## Cramer's V (phi): 0.018
##
## Chi-square Test of Independence:
## Chisq = 0.037, df = 1, p-value = 0.848
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## together_usable_b
## bgm_qus_talking_1_28 0 1
## 0 0.889 0.908
## 1 0.111 0.092
## Sum 1.000 1.000
## >>> Suggestions
## Plot(alone_usable_b, bgm_qus_talking_1_28) # bubble plot
## BarChart(alone_usable_b, by=bgm_qus_talking_1_28, horiz=TRUE) # horizontal bar chart
## BarChart(alone_usable_b, fill="steelblue") # steelblue bars
##
## Joint and Marginal Frequencies
## ------------------------------
##
## alone_usable_b
## bgm_qus_talking_1_28 0 1 Sum
## 0 9 79 88
## 1 2 6 8
## Sum 11 85 96
##
## Cramer's V (phi): 0.128
##
## Chi-square Test of Independence:
## Chisq = 1.577, df = 1, p-value = 0.209
## >>> Low cell expected frequencies, chi-squared approximation may not be accurate
##
## Cell Proportions within Each Column
## -----------------------------------
##
## alone_usable_b
## bgm_qus_talking_1_28 0 1
## 0 0.818 0.929
## 1 0.182 0.071
## Sum 1.000 1.000Associations with baby variables RSA
# Child gender
lessR::tt_brief(RSA_tog ~ child_gender_final, data = data)##
## Compare RSA_tog across child_gender_final with levels 1 and 2
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: child_gender_final,
##
##
## --- Describe ---
##
## RSA_tog for child_gender_final 1: n.miss = 9, n = 61, mean = 2.585839827, sd = 1.091141160
## RSA_tog for child_gender_final 2: n.miss = 6, n = 44, mean = 2.578227819, sd = 0.947491734
##
## Mean Difference of RSA_tog: 0.007612008
## Weighted Average Standard Deviation: 1.033601429
## Standardized Mean Difference of RSA_tog: 0.007364549
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.204435586
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.037, df = 103, p-value = 0.970
##
## Margin of Error for 95% Confidence Level: 0.405449767
## 95% Confidence Interval for Mean Difference: -0.397837759 to 0.413061775
lessR::tt_brief(RSA_alone ~ child_gender_final, data = data)##
## Compare RSA_alone across child_gender_final with levels 1 and 2
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: child_gender_final,
##
##
## --- Describe ---
##
## RSA_alone for child_gender_final 1: n.miss = 23, n = 47, mean = 2.65577579, sd = 1.24492147
## RSA_alone for child_gender_final 2: n.miss = 14, n = 36, mean = 2.54898023, sd = 1.17530588
##
## Mean Difference of RSA_alone: 0.10679556
## Weighted Average Standard Deviation: 1.21533003
## Standardized Mean Difference of RSA_alone: 0.08787371
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.26917399
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.397, df = 81, p-value = 0.693
##
## Margin of Error for 95% Confidence Level: 0.53557181
## 95% Confidence Interval for Mean Difference: -0.42877625 to 0.64236737
# BIRTH --> NOTHING
table(data$underweight_birth)##
## 0 1
## 98 10lessR::tt_brief(RSA_tog ~ underweight_birth, data = data)##
## Compare RSA_tog across underweight_birth with levels 1 and 0
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: underweight_birth,
##
##
## --- Describe ---
##
## RSA_tog for underweight_birth 1: n.miss = 1, n = 9, mean = 2.56959139, sd = 1.07481496
## RSA_tog for underweight_birth 0: n.miss = 13, n = 85, mean = 2.55146442, sd = 1.00555623
##
## Mean Difference of RSA_tog: 0.01812697
## Weighted Average Standard Deviation: 1.01176695
## Standardized Mean Difference of RSA_tog: 0.01791615
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.35466122
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.051, df = 92, p-value = 0.959
##
## Margin of Error for 95% Confidence Level: 0.70438779
## 95% Confidence Interval for Mean Difference: -0.68626082 to 0.72251476
lessR::tt_brief(RSA_alone ~ underweight_birth, data = data)##
## Compare RSA_alone across underweight_birth with levels 0 and 1
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: underweight_birth,
##
##
## --- Describe ---
##
## RSA_alone for underweight_birth 0: n.miss = 34, n = 64, mean = 2.62959265, sd = 1.14112460
## RSA_alone for underweight_birth 1: n.miss = 2, n = 8, mean = 2.22102666, sd = 1.70021967
##
## Mean Difference of RSA_alone: 0.40856599
## Weighted Average Standard Deviation: 1.20872806
## Standardized Mean Difference of RSA_alone: 0.33801316
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.994
## Standard Error of Mean Difference: SE = 0.45327302
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.901, df = 70, p-value = 0.370
##
## Margin of Error for 95% Confidence Level: 0.90402454
## 95% Confidence Interval for Mean Difference: -0.49545854 to 1.31259053
table(data$weight_category_birth)##
## Low Birth Weight Normal Birth Weight
## 15 93lessR::tt_brief(RSA_tog ~ weight_category_birth, data = data)##
## Compare RSA_tog across weight_category_birth with levels Low Birth Weight and Normal Birth Weight
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: weight_category_birth,
##
##
## --- Describe ---
##
## RSA_tog for weight_category_birth Low Birth Weight: n.miss = 2, n = 13, mean = 2.6496218, sd = 0.9507491
## RSA_tog for weight_category_birth Normal Birth Weight: n.miss = 12, n = 81, mean = 2.5377249, sd = 1.0197621
##
## Mean Difference of RSA_tog: 0.1118970
## Weighted Average Standard Deviation: 1.0110276
## Standardized Mean Difference of RSA_tog: 0.1106765
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.3020736
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.370, df = 92, p-value = 0.712
##
## Margin of Error for 95% Confidence Level: 0.5999442
## 95% Confidence Interval for Mean Difference: -0.4880473 to 0.7118412
lessR::tt_brief(RSA_alone ~ weight_category_birth, data = data)##
## Compare RSA_alone across weight_category_birth with levels Normal Birth Weight and Low Birth Weight
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: weight_category_birth,
##
##
## --- Describe ---
##
## RSA_alone for weight_category_birth Normal Birth Weight: n.miss = 31, n = 62, mean = 2.62419412, sd = 1.15558659
## RSA_alone for weight_category_birth Low Birth Weight: n.miss = 5, n = 10, mean = 2.33621079, sd = 1.53790322
##
## Mean Difference of RSA_alone: 0.28798333
## Weighted Average Standard Deviation: 1.21151923
## Standardized Mean Difference of RSA_alone: 0.23770430
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.994
## Standard Error of Mean Difference: SE = 0.41285801
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.698, df = 70, p-value = 0.488
##
## Margin of Error for 95% Confidence Level: 0.82341934
## 95% Confidence Interval for Mean Difference: -0.53543601 to 1.11140267
table(data$wasting_birth)##
## 0 1
## 93 12lessR::tt_brief(RSA_tog ~ wasting_birth, data = data)##
## Compare RSA_tog across wasting_birth with levels 1 and 0
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: wasting_birth,
##
##
## --- Describe ---
##
## RSA_tog for wasting_birth 1: n.miss = 3, n = 9, mean = 3.11476901, sd = 1.01332659
## RSA_tog for wasting_birth 0: n.miss = 11, n = 82, mean = 2.52319494, sd = 0.99450551
##
## Mean Difference of RSA_tog: 0.59157408
## Weighted Average Standard Deviation: 0.99621183
## Standardized Mean Difference of RSA_tog: 0.59382358
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.987
## Standard Error of Mean Difference: SE = 0.34981966
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.691, df = 89, p-value = 0.094
##
## Margin of Error for 95% Confidence Level: 0.69508421
## 95% Confidence Interval for Mean Difference: -0.10351013 to 1.28665828
lessR::tt_brief(RSA_alone ~ wasting_birth, data = data)##
## Compare RSA_alone across wasting_birth with levels 1 and 0
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: wasting_birth,
##
##
## --- Describe ---
##
## RSA_alone for wasting_birth 1: n.miss = 3, n = 9, mean = 2.81382973, sd = 1.55988891
## RSA_alone for wasting_birth 0: n.miss = 33, n = 60, mean = 2.57464909, sd = 1.16286986
##
## Mean Difference of RSA_alone: 0.23918064
## Weighted Average Standard Deviation: 1.21710287
## Standardized Mean Difference of RSA_alone: 0.19651638
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.996
## Standard Error of Mean Difference: SE = 0.43506581
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.550, df = 67, p-value = 0.584
##
## Margin of Error for 95% Confidence Level: 0.86839498
## 95% Confidence Interval for Mean Difference: -0.62921434 to 1.10757563
table(data$stunting_birth)##
## 0 1
## 102 5lessR::tt_brief(RSA_tog ~ stunting_birth, data = data)##
## Compare RSA_tog across stunting_birth with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: stunting_birth,
##
##
## --- Describe ---
##
## RSA_tog for stunting_birth 0: n.miss = 13, n = 89, mean = 2.55843558, sd = 1.02419738
## RSA_tog for stunting_birth 1: n.miss = 1, n = 4, mean = 2.48299826, sd = 0.77756667
##
## Mean Difference of RSA_tog: 0.07543732
## Weighted Average Standard Deviation: 1.01702051
## Standardized Mean Difference of RSA_tog: 0.07417483
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.986
## Standard Error of Mean Difference: SE = 0.51981186
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.145, df = 91, p-value = 0.885
##
## Margin of Error for 95% Confidence Level: 1.03254241
## 95% Confidence Interval for Mean Difference: -0.95710509 to 1.10797972
lessR::tt_brief(RSA_alone ~ stunting_birth, data = data)##
## Compare RSA_alone across stunting_birth with levels 0 and 1
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: stunting_birth,
##
##
## --- Describe ---
##
## RSA_alone for stunting_birth 0: n.miss = 34, n = 68, mean = 2.58650841, sd = 1.15766460
## RSA_alone for stunting_birth 1: n.miss = 2, n = 3, mean = 2.24019048, sd = 2.50454197
##
## Mean Difference of RSA_alone: 0.34631793
## Weighted Average Standard Deviation: 1.21785040
## Standardized Mean Difference of RSA_alone: 0.28436820
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.995
## Standard Error of Mean Difference: SE = 0.71846900
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.482, df = 69, p-value = 0.631
##
## Margin of Error for 95% Confidence Level: 1.43330643
## 95% Confidence Interval for Mean Difference: -1.08698850 to 1.77962436
table(data$less_than_full_term)##
## 0 1
## 81 39lessR::tt_brief(RSA_tog ~ less_than_full_term, data = data)##
## Compare RSA_tog across less_than_full_term with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: less_than_full_term,
##
##
## --- Describe ---
##
## RSA_tog for less_than_full_term 0: n.miss = 11, n = 70, mean = 2.63818571, sd = 1.04981075
## RSA_tog for less_than_full_term 1: n.miss = 4, n = 35, mean = 2.47157869, sd = 0.99034907
##
## Mean Difference of RSA_tog: 0.16660702
## Weighted Average Standard Deviation: 1.03056203
## Standardized Mean Difference of RSA_tog: 0.16166617
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.21334661
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.781, df = 103, p-value = 0.437
##
## Margin of Error for 95% Confidence Level: 0.42312268
## 95% Confidence Interval for Mean Difference: -0.25651566 to 0.58972970
lessR::tt_brief(RSA_alone ~ less_than_full_term, data = data)##
## Compare RSA_alone across less_than_full_term with levels 0 and 1
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: less_than_full_term,
##
##
## --- Describe ---
##
## RSA_alone for less_than_full_term 0: n.miss = 27, n = 54, mean = 2.61834432, sd = 1.09151189
## RSA_alone for less_than_full_term 1: n.miss = 10, n = 29, mean = 2.59290197, sd = 1.42321790
##
## Mean Difference of RSA_alone: 0.02544235
## Weighted Average Standard Deviation: 1.21644840
## Standardized Mean Difference of RSA_alone: 0.02091527
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.28005083
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.091, df = 81, p-value = 0.928
##
## Margin of Error for 95% Confidence Level: 0.55721330
## 95% Confidence Interval for Mean Difference: -0.53177094 to 0.58265565
# 28 DAYS --> NOTHING
table(data$underweight_28)##
## 0 1
## 102 17lessR::tt_brief(RSA_tog ~ underweight_28, data = data)##
## Compare RSA_tog across underweight_28 with levels 1 and 0
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: underweight_28,
##
##
## --- Describe ---
##
## RSA_tog for underweight_28 1: n.miss = 1, n = 16, mean = 2.812492160, sd = 0.805595271
## RSA_tog for underweight_28 0: n.miss = 14, n = 88, mean = 2.530834396, sd = 1.063743925
##
## Mean Difference of RSA_tog: 0.281657763
## Weighted Average Standard Deviation: 1.029847243
## Standardized Mean Difference of RSA_tog: 0.273494701
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.279890497
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.006, df = 102, p-value = 0.317
##
## Margin of Error for 95% Confidence Level: 0.555161473
## 95% Confidence Interval for Mean Difference: -0.273503710 to 0.836819237
lessR::tt_brief(RSA_alone ~ underweight_28, data = data)##
## Compare RSA_alone across underweight_28 with levels 1 and 0
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: underweight_28,
##
##
## --- Describe ---
##
## RSA_alone for underweight_28 1: n.miss = 4, n = 13, mean = 3.08836706, sd = 1.51653899
## RSA_alone for underweight_28 0: n.miss = 33, n = 69, mean = 2.51000939, sd = 1.13891501
##
## Mean Difference of RSA_alone: 0.57835767
## Weighted Average Standard Deviation: 1.20313834
## Standardized Mean Difference of RSA_alone: 0.48070754
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.36376949
##
## Hypothesis Test of 0 Mean Diff: t-value = 1.590, df = 80, p-value = 0.116
##
## Margin of Error for 95% Confidence Level: 0.72392436
## 95% Confidence Interval for Mean Difference: -0.14556669 to 1.30228202
table(data$wasting_28)##
## 0 1
## 99 20lessR::tt_brief(RSA_tog ~ wasting_28, data = data)##
## Compare RSA_tog across wasting_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: wasting_28,
##
##
## --- Describe ---
##
## RSA_tog for wasting_28 0: n.miss = 15, n = 84, mean = 2.57807037, sd = 1.02098443
## RSA_tog for wasting_28 1: n.miss = 0, n = 20, mean = 2.55776951, sd = 1.09369256
##
## Mean Difference of RSA_tog: 0.02030086
## Weighted Average Standard Deviation: 1.03491531
## Standardized Mean Difference of RSA_tog: 0.01961597
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.25749384
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.079, df = 102, p-value = 0.937
##
## Margin of Error for 95% Confidence Level: 0.51073781
## 95% Confidence Interval for Mean Difference: -0.49043694 to 0.53103867
lessR::tt_brief(RSA_alone ~ wasting_28, data = data)##
## Compare RSA_alone across wasting_28 with levels 1 and 0
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: wasting_28,
##
##
## --- Describe ---
##
## RSA_alone for wasting_28 1: n.miss = 6, n = 14, mean = 2.66541450, sd = 1.44411594
## RSA_alone for wasting_28 0: n.miss = 31, n = 68, mean = 2.58858260, sd = 1.17360760
##
## Mean Difference of RSA_alone: 0.07683189
## Weighted Average Standard Deviation: 1.22164793
## Standardized Mean Difference of RSA_alone: 0.06289201
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.35853744
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.214, df = 80, p-value = 0.831
##
## Margin of Error for 95% Confidence Level: 0.71351225
## 95% Confidence Interval for Mean Difference: -0.63668035 to 0.79034414
table(data$stunting_28)##
## 0 1
## 112 7lessR::tt_brief(RSA_tog ~ stunting_28, data = data)##
## Compare RSA_tog across stunting_28 with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: stunting_28,
##
##
## --- Describe ---
##
## RSA_tog for stunting_28 0: n.miss = 15, n = 97, mean = 2.584494538, sd = 1.037451582
## RSA_tog for stunting_28 1: n.miss = 0, n = 7, mean = 2.431047311, sd = 0.981040457
##
## Mean Difference of RSA_tog: 0.153447227
## Weighted Average Standard Deviation: 1.034218458
## Standardized Mean Difference of RSA_tog: 0.148370227
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.404756719
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.379, df = 102, p-value = 0.705
##
## Margin of Error for 95% Confidence Level: 0.802833034
## 95% Confidence Interval for Mean Difference: -0.649385806 to 0.956280261
#lessR::tt_brief(RSA_alone ~ stunting_28, data = data)
table(data$less_than_full_term)##
## 0 1
## 81 39lessR::tt_brief(RSA_tog ~ less_than_full_term, data = data)##
## Compare RSA_tog across less_than_full_term with levels 0 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: less_than_full_term,
##
##
## --- Describe ---
##
## RSA_tog for less_than_full_term 0: n.miss = 11, n = 70, mean = 2.63818571, sd = 1.04981075
## RSA_tog for less_than_full_term 1: n.miss = 4, n = 35, mean = 2.47157869, sd = 0.99034907
##
## Mean Difference of RSA_tog: 0.16660702
## Weighted Average Standard Deviation: 1.03056203
## Standardized Mean Difference of RSA_tog: 0.16166617
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.21334661
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.781, df = 103, p-value = 0.437
##
## Margin of Error for 95% Confidence Level: 0.42312268
## 95% Confidence Interval for Mean Difference: -0.25651566 to 0.58972970
lessR::tt_brief(RSA_alone ~ less_than_full_term, data = data)##
## Compare RSA_alone across less_than_full_term with levels 0 and 1
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: less_than_full_term,
##
##
## --- Describe ---
##
## RSA_alone for less_than_full_term 0: n.miss = 27, n = 54, mean = 2.61834432, sd = 1.09151189
## RSA_alone for less_than_full_term 1: n.miss = 10, n = 29, mean = 2.59290197, sd = 1.42321790
##
## Mean Difference of RSA_alone: 0.02544235
## Weighted Average Standard Deviation: 1.21644840
## Standardized Mean Difference of RSA_alone: 0.02091527
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.28005083
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.091, df = 81, p-value = 0.928
##
## Margin of Error for 95% Confidence Level: 0.55721330
## 95% Confidence Interval for Mean Difference: -0.53177094 to 0.58265565
lessR::tt_brief(RSA_tog ~ restype, data = data)##
## Compare RSA_tog across restype with levels 2 and 1
## Response Variable: RSA_tog, RSA_tog
## Grouping Variable: restype,
##
##
## --- Describe ---
##
## RSA_tog for restype 2: n.miss = 1, n = 25, mean = 2.714849218, sd = 0.884491427
## RSA_tog for restype 1: n.miss = 14, n = 80, mean = 2.541337788, sd = 1.071438253
##
## Mean Difference of RSA_tog: 0.173511430
## Weighted Average Standard Deviation: 1.030911636
## Standardized Mean Difference of RSA_tog: 0.168308732
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.983
## Standard Error of Mean Difference: SE = 0.236211530
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.735, df = 103, p-value = 0.464
##
## Margin of Error for 95% Confidence Level: 0.468469859
## 95% Confidence Interval for Mean Difference: -0.294958429 to 0.641981289
lessR::tt_brief(RSA_alone ~ restype, data = data)##
## Compare RSA_alone across restype with levels 2 and 1
## Response Variable: RSA_alone, RSA_alone
## Grouping Variable: restype,
##
##
## --- Describe ---
##
## RSA_alone for restype 2: n.miss = 12, n = 14, mean = 2.83633301, sd = 0.94133750
## RSA_alone for restype 1: n.miss = 25, n = 69, mean = 2.56342157, sd = 1.25724569
##
## Mean Difference of RSA_alone: 0.27291143
## Weighted Average Standard Deviation: 1.21210382
## Standardized Mean Difference of RSA_alone: 0.22515516
##
## --- Infer ---
##
## t-cutoff for 95% range of variation: tcut = 1.990
## Standard Error of Mean Difference: SE = 0.35529599
##
## Hypothesis Test of 0 Mean Diff: t-value = 0.768, df = 81, p-value = 0.445
##
## Margin of Error for 95% Confidence Level: 0.70692757
## 95% Confidence Interval for Mean Difference: -0.43401613 to 0.97983900
####### ZSCORES
data %>% correlation(p_adjust = "none", select = c("RSA_alone", "RSA_tog"), select2 = c( "child_age_final", "child_ga_final","wflz_28" , "wfaz_28" ,"lfaz_28" , "hcaz_28" , "wflz_birth", "wfaz_birth" , "lfaz_birth" ,"hcaz_birth", "average_birth_muac" ,"average_baby_muac.y"), method = "pearson")## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t | df | p
## ----------------------------------------------------------------------------------------
## RSA_alone | child_age_final | 0.08 | [-0.14, 0.29] | 0.72 | 81 | 0.471
## RSA_alone | child_ga_final | -0.03 | [-0.25, 0.19] | -0.26 | 80 | 0.792
## RSA_alone | wflz_28 | 0.05 | [-0.17, 0.26] | 0.43 | 80 | 0.666
## RSA_alone | wfaz_28 | -0.08 | [-0.29, 0.14] | -0.70 | 80 | 0.486
## RSA_alone | lfaz_28 | -0.13 | [-0.34, 0.09] | -1.15 | 80 | 0.254
## RSA_alone | hcaz_28 | -0.10 | [-0.31, 0.12] | -0.93 | 80 | 0.357
## RSA_alone | wflz_birth | 7.37e-03 | [-0.23, 0.24] | 0.06 | 67 | 0.952
## RSA_alone | wfaz_birth | 0.05 | [-0.18, 0.28] | 0.45 | 70 | 0.651
## RSA_alone | lfaz_birth | -0.02 | [-0.25, 0.21] | -0.18 | 69 | 0.857
## RSA_alone | hcaz_birth | 0.07 | [-0.17, 0.29] | 0.55 | 70 | 0.587
## RSA_alone | average_birth_muac | 0.16 | [-0.08, 0.38] | 1.35 | 70 | 0.181
## RSA_alone | average_baby_muac.y | 2.79e-03 | [-0.21, 0.22] | 0.02 | 80 | 0.980
## RSA_tog | child_age_final | 0.04 | [-0.16, 0.23] | 0.38 | 103 | 0.702
## RSA_tog | child_ga_final | 0.07 | [-0.13, 0.25] | 0.67 | 103 | 0.502
## RSA_tog | wflz_28 | -0.08 | [-0.27, 0.11] | -0.83 | 102 | 0.406
## RSA_tog | wfaz_28 | -0.08 | [-0.27, 0.11] | -0.83 | 102 | 0.409
## RSA_tog | lfaz_28 | -0.02 | [-0.21, 0.18] | -0.16 | 102 | 0.873
## RSA_tog | hcaz_28 | -0.22 | [-0.39, -0.03] | -2.24 | 102 | 0.027*
## RSA_tog | wflz_birth | -0.04 | [-0.25, 0.17] | -0.39 | 89 | 0.698
## RSA_tog | wfaz_birth | -3.34e-04 | [-0.20, 0.20] | -3.20e-03 | 92 | 0.997
## RSA_tog | lfaz_birth | -0.06 | [-0.26, 0.15] | -0.54 | 91 | 0.593
## RSA_tog | hcaz_birth | -0.10 | [-0.29, 0.11] | -0.92 | 92 | 0.359
## RSA_tog | average_birth_muac | 0.09 | [-0.12, 0.29] | 0.85 | 92 | 0.397
## RSA_tog | average_baby_muac.y | -0.03 | [-0.23, 0.16] | -0.35 | 102 | 0.725
##
## p-value adjustment method: none
## Observations: 69-105data %>% filter(bgc_avg > 3) %>% correlation(p_adjust = "none", select = c("RSA_alone", "RSA_tog"), select2 = c( "child_age_final","child_ga_final","wflz_28" , "wfaz_28" ,"lfaz_28" , "hcaz_28" , "wflz_birth", "wfaz_birth" , "lfaz_birth" ,"hcaz_birth", "average_birth_muac" ,"average_baby_muac.y"), method = "pearson")## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t | df | p
## ---------------------------------------------------------------------------------------
## RSA_alone | child_age_final | 0.07 | [-0.21, 0.34] | 0.50 | 50 | 0.622
## RSA_alone | child_ga_final | 0.06 | [-0.21, 0.33] | 0.46 | 50 | 0.650
## RSA_alone | wflz_28 | 3.32e-03 | [-0.27, 0.28] | 0.02 | 50 | 0.981
## RSA_alone | wfaz_28 | -0.06 | [-0.33, 0.22] | -0.41 | 50 | 0.685
## RSA_alone | lfaz_28 | -0.07 | [-0.34, 0.21] | -0.49 | 50 | 0.624
## RSA_alone | hcaz_28 | -9.74e-04 | [-0.27, 0.27] | -6.89e-03 | 50 | 0.995
## RSA_alone | wflz_birth | 0.42 | [ 0.13, 0.65] | 2.91 | 39 | 0.006**
## RSA_alone | wfaz_birth | 0.33 | [ 0.04, 0.57] | 2.28 | 42 | 0.028*
## RSA_alone | lfaz_birth | 0.07 | [-0.24, 0.36] | 0.43 | 41 | 0.671
## RSA_alone | hcaz_birth | 0.15 | [-0.15, 0.43] | 1.01 | 42 | 0.316
## RSA_alone | average_birth_muac | 0.32 | [ 0.02, 0.56] | 2.18 | 42 | 0.035*
## RSA_alone | average_baby_muac.y | 0.04 | [-0.24, 0.31] | 0.26 | 50 | 0.799
## RSA_tog | child_age_final | 0.02 | [-0.24, 0.27] | 0.14 | 57 | 0.886
## RSA_tog | child_ga_final | 0.09 | [-0.17, 0.34] | 0.67 | 57 | 0.504
## RSA_tog | wflz_28 | -9.48e-03 | [-0.26, 0.25] | -0.07 | 57 | 0.943
## RSA_tog | wfaz_28 | -0.06 | [-0.31, 0.20] | -0.48 | 57 | 0.636
## RSA_tog | lfaz_28 | -0.05 | [-0.30, 0.21] | -0.39 | 57 | 0.699
## RSA_tog | hcaz_28 | -0.11 | [-0.36, 0.15] | -0.83 | 57 | 0.409
## RSA_tog | wflz_birth | 0.16 | [-0.13, 0.43] | 1.08 | 45 | 0.285
## RSA_tog | wfaz_birth | 0.11 | [-0.17, 0.38] | 0.79 | 48 | 0.432
## RSA_tog | lfaz_birth | -0.09 | [-0.36, 0.19] | -0.64 | 47 | 0.528
## RSA_tog | hcaz_birth | -0.08 | [-0.35, 0.21] | -0.54 | 48 | 0.594
## RSA_tog | average_birth_muac | 0.16 | [-0.13, 0.42] | 1.10 | 48 | 0.278
## RSA_tog | average_baby_muac.y | 0.04 | [-0.21, 0.30] | 0.33 | 57 | 0.741
##
## p-value adjustment method: none
## Observations: 41-59data %>% filter(bgc_avg <4) %>% correlation(p_adjust = "none", select = c("RSA_alone", "RSA_tog"), select2 = c( "child_age_final","child_ga_final","wflz_28" , "wfaz_28" ,"lfaz_28" , "hcaz_28" , "wflz_birth", "wfaz_birth" , "lfaz_birth" ,"hcaz_birth", "average_birth_muac" ,"average_baby_muac.y"), method = "pearson")## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t | df | p
## -------------------------------------------------------------------------------------
## RSA_alone | child_age_final | 0.36 | [ 0.02, 0.63] | 2.18 | 31 | 0.037*
## RSA_alone | child_ga_final | -0.28 | [-0.57, 0.07] | -1.61 | 30 | 0.117
## RSA_alone | wflz_28 | 0.04 | [-0.31, 0.38] | 0.23 | 31 | 0.823
## RSA_alone | wfaz_28 | -0.18 | [-0.50, 0.17] | -1.04 | 31 | 0.307
## RSA_alone | lfaz_28 | -0.22 | [-0.52, 0.13] | -1.26 | 31 | 0.216
## RSA_alone | hcaz_28 | -0.21 | [-0.52, 0.14] | -1.19 | 31 | 0.242
## RSA_alone | wflz_birth | -0.38 | [-0.65, -0.01] | -2.11 | 27 | 0.044*
## RSA_alone | wfaz_birth | -0.32 | [-0.61, 0.06] | -1.73 | 27 | 0.095
## RSA_alone | lfaz_birth | -0.10 | [-0.45, 0.28] | -0.50 | 27 | 0.621
## RSA_alone | hcaz_birth | 1.06e-03 | [-0.37, 0.37] | 5.49e-03 | 27 | 0.996
## RSA_alone | average_birth_muac | -0.04 | [-0.40, 0.33] | -0.23 | 27 | 0.818
## RSA_alone | average_baby_muac.y | -0.07 | [-0.41, 0.28] | -0.42 | 31 | 0.680
## RSA_tog | child_age_final | 0.22 | [-0.07, 0.48] | 1.50 | 44 | 0.140
## RSA_tog | child_ga_final | 0.02 | [-0.27, 0.31] | 0.13 | 44 | 0.899
## RSA_tog | wflz_28 | -0.17 | [-0.44, 0.13] | -1.14 | 44 | 0.259
## RSA_tog | wfaz_28 | -0.18 | [-0.44, 0.12] | -1.19 | 44 | 0.239
## RSA_tog | lfaz_28 | -0.06 | [-0.35, 0.23] | -0.42 | 44 | 0.673
## RSA_tog | hcaz_28 | -0.28 | [-0.53, 0.01] | -1.92 | 44 | 0.061
## RSA_tog | wflz_birth | -0.16 | [-0.44, 0.14] | -1.06 | 41 | 0.297
## RSA_tog | wfaz_birth | -0.15 | [-0.43, 0.16] | -0.98 | 41 | 0.333
## RSA_tog | lfaz_birth | -0.08 | [-0.37, 0.23] | -0.51 | 41 | 0.615
## RSA_tog | hcaz_birth | -0.06 | [-0.35, 0.25] | -0.37 | 41 | 0.716
## RSA_tog | average_birth_muac | -0.02 | [-0.32, 0.28] | -0.11 | 41 | 0.912
## RSA_tog | average_baby_muac.y | -0.14 | [-0.42, 0.15] | -0.96 | 44 | 0.344
##
## p-value adjustment method: none
## Observations: 29-46######
s <- lm(RSA_alone ~ bgc_avg*wflz_birth + wflz_28 + child_ga_final + primiparous + child_age_final, data = data)
summary(s)##
## Call:
## lm(formula = RSA_alone ~ bgc_avg * wflz_birth + wflz_28 + child_ga_final +
## primiparous + child_age_final, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.07342 -0.72472 -0.07827 0.69814 2.49853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2569916 5.0950878 1.032 0.306388
## bgc_avg 0.0370627 0.1191189 0.311 0.756790
## wflz_birth -0.8124416 0.2600956 -3.124 0.002768 **
## wflz_28 -0.0900779 0.1296987 -0.695 0.490084
## child_ga_final -0.0724659 0.1259341 -0.575 0.567191
## primiparousNon-primiparous 0.2544618 0.2892039 0.880 0.382500
## child_age_final 0.0004034 0.0099123 0.041 0.967675
## bgc_avg:wflz_birth 0.2622007 0.0739488 3.546 0.000774 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.086 on 59 degrees of freedom
## (75 observations deleted due to missingness)
## Multiple R-squared: 0.2766, Adjusted R-squared: 0.1907
## F-statistic: 3.222 on 7 and 59 DF, p-value: 0.005838library(interactions)
probe_interaction(model = s, pred = wflz_birth, modx = bgc_avg, interval = T, jnplot = T)## JOHNSON-NEYMAN INTERVAL
##
## When bgc_avg is OUTSIDE the interval [2.01, 4.13], the slope of wflz_birth
## is p < .05.
##
## Note: The range of observed values of bgc_avg is [1.00, 6.00]
## SIMPLE SLOPES ANALYSIS
##
## Slope of wflz_birth when bgc_avg = 2.093946 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.26 0.14 -1.90 0.06
##
## Slope of wflz_birth when bgc_avg = 3.694030 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.16 0.12 1.29 0.20
##
## Slope of wflz_birth when bgc_avg = 5.294113 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.58 0.20 2.95 0.00
t <- lm(RSA_alone ~ bgc_avg*average_birth_muac + average_baby_muac.y + child_ga_final + child_age_final+ primiparous, data = data)
summary(t)##
## Call:
## lm(formula = RSA_alone ~ bgc_avg * average_birth_muac + average_baby_muac.y +
## child_ga_final + child_age_final + primiparous, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7700 -0.8155 -0.1340 0.7917 3.1431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.06888 5.93581 2.707 0.00876 **
## bgc_avg -2.48976 0.90167 -2.761 0.00756 **
## average_birth_muac -0.63636 0.41483 -1.534 0.13011
## average_baby_muac.y -0.26815 0.17298 -1.550 0.12619
## child_ga_final -0.09651 0.12974 -0.744 0.45974
## child_age_final 0.01075 0.01084 0.992 0.32500
## primiparousNon-primiparous 0.28565 0.30502 0.936 0.35265
## bgc_avg:average_birth_muac 0.22732 0.09144 2.486 0.01563 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 62 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: 0.2459, Adjusted R-squared: 0.1607
## F-statistic: 2.888 on 7 and 62 DF, p-value: 0.0112interact_plot(t, pred = average_birth_muac, modx = bgc_avg, interval = T, jnplot = T)
sim_slopes(t, pred = average_birth_muac, modx = bgc_avg, interval = T, jnplot = T)## JOHNSON-NEYMAN INTERVAL
##
## When bgc_avg is OUTSIDE the interval [-3.54, 4.37], the slope of
## average_birth_muac is p < .05.
##
## Note: The range of observed values of bgc_avg is [1.00, 6.00]
## SIMPLE SLOPES ANALYSIS
##
## Slope of average_birth_muac when bgc_avg = 2.162899 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.14 0.25 -0.58 0.57
##
## Slope of average_birth_muac when bgc_avg = 3.757143 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.22 0.18 1.21 0.23
##
## Slope of average_birth_muac when bgc_avg = 5.351387 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.58 0.21 2.76 0.01data_w <- data %>% filter(wflz_birth > -4 & wflz_birth < 3)
data_long <- data_w %>%
pivot_longer(
cols = c(average_birth_muac, wflz_birth),
names_to = "Anthropometrics",
values_to = "Anthropometric_value"
)
# Add the Arousal State
data_long <- data_long %>%
mutate(ArousalState = case_when(
bgc_avg < 4 ~ "Low arousal state",
bgc_avg >= 4 ~ "High arousal state"
)) %>%
filter(!is.na(ArousalState))
# Plot with reshaped data
library(ggplot2)
# Updated plot with non-bold labels and custom theme
ggplot(data_long, aes(x = Anthropometric_value, y = RSA_alone, color = ArousalState)) +
geom_point(size = 3) +
geom_smooth(method = "lm", size = 0.8, color = "black", alpha = 0.1) +
facet_grid(ArousalState ~ Anthropometrics, scales = "free_x",
labeller = labeller(Anthropometrics = c(average_birth_muac = "MUAC at birth (cms)",
wflz_birth = "Weight-for-length at birth"))) +
theme_modern() +
theme(
panel.spacing = unit(1.2, "lines"), # Adds spacing between high and low arousal panels
strip.text = element_text(size = 14, face = "plain"), # Keeps panel labels non-bold
axis.text = element_text(size = 12),
legend.position = "none", # Removes the legend
axis.title = element_text(size = 15, face = "bold") # Keeps axis titles non-bold
) +
scale_color_manual(values = poster) +
scale_y_continuous(breaks = seq(1, 6, 0.5), expand = expansion(mult = 0.05)) + # More detailed RSA scale
scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) + # Adjust anthropometric scale
labs(
x = "Anthropometrics",
y = "Infant solo RSA",
title = "Infant RSA & Anthropometrics by Arousal State"
)## `geom_smooth()` using formula = 'y ~ x'
ggsave("AnthroResults.png", width = 6, height = 5, unit = "in", dpi = 300, bg = 'white')## `geom_smooth()` using formula = 'y ~ x'#ggsave("pwixsx_leg.png", width = 10, height = 6, unit = "in", dpi = 300, bg = 'white')Descriptives
#desc <- data %>% select(wflz_birth) %>% describe_distribution()
desc <- data %>% select(child_ga_final) %>% describe_distribution()
desc## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## --------------------------------------------------------------------------------------------
## child_ga_final | 38.94 | 1.50 | 2 | [30.00, 42.00] | -1.95 | 10.71 | 119 | 23desc <- data %>% select(RSA_alone, RSA_tog) %>% describe_distribution()
desc## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## -------------------------------------------------------------------------------------
## RSA_alone | 2.61 | 1.21 | 1.61 | [0.43, 5.76] | 0.48 | -0.30 | 83 | 59
## RSA_tog | 2.58 | 1.03 | 1.55 | [0.09, 4.64] | -0.05 | -0.57 | 105 | 37desc <- data %>% select(BMI) %>% describe_distribution()
desc## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## ---------------------------------------------------------------------------------------
## BMI | 23.73 | 4.59 | 3.79 | [16.15, 60.00] | 4.45 | 32.65 | 119 | 23desc <- data %>% select(average_birth_length, average_birth_muac, average_birth_weight,
average_baby_length.y, average_baby_weight.y, average_baby_muac.y) %>% describe_distribution()
desc## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## ----------------------------------------------------------------------------------------------------
## average_birth_length | 49.05 | 2.27 | 2.87 | [37.10, 55.25] | -1.11 | 6.26 | 108 | 34
## average_birth_muac | 9.88 | 0.90 | 1.20 | [8.04, 12.50] | 0.33 | 0.07 | 108 | 34
## average_birth_weight | 2.96 | 0.49 | 0.58 | [1.64, 4.66] | 0.48 | 1.76 | 108 | 34
## average_baby_length.y | 55.34 | 2.80 | 3.40 | [47.90, 63.25] | 0.34 | 0.82 | 119 | 23
## average_baby_weight.y | 4.33 | 0.67 | 0.79 | [2.54, 5.98] | -0.11 | 0.56 | 119 | 23
## average_baby_muac.y | 11.89 | 0.99 | 1.20 | [8.45, 14.75] | -0.63 | 1.55 | 119 | 23data %>% select(RSA_alone, RSA_tog) %>% describe_distribution()## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## -------------------------------------------------------------------------------------
## RSA_alone | 2.61 | 1.21 | 1.61 | [0.43, 5.76] | 0.48 | -0.30 | 83 | 59
## RSA_tog | 2.58 | 1.03 | 1.55 | [0.09, 4.64] | -0.05 | -0.57 | 105 | 37# Load necessary library
library(dplyr)
# Calculate median for continuous variables and percentage for gender variable
summary_stats <- data %>%
summarise(
median_q102b_guess_age = median(q102b_guess_age, na.rm = TRUE),
median_child_age_final = median(child_age_final, na.rm = TRUE),
male_count = sum(child_gender_final == 1, na.rm = TRUE),
female_count = sum(child_gender_final == 2, na.rm = TRUE),
male_percentage = (male_count / n()) * 100,
female_percentage = (female_count / n()) * 100
)
# Display the results
summary_stats## # A tibble: 1 × 6
## median_q102b_guess_age median_child_age_final male_count female_count
## <dbl> <dbl> <int> <int>
## 1 19 43.4 70 50
## # ℹ 2 more variables: male_percentage <dbl>, female_percentage <dbl>data %>% count(child_gender_final)## # A tibble: 3 × 2
## child_gender_final n
## <dbl+lbl> <int>
## 1 1 [Male] 70
## 2 2 [Female] 50
## 3 NA 22data %>% count(restype)## # A tibble: 3 × 2
## restype n
## <dbl+lbl> <int>
## 1 1 [FDMN Camp] 94
## 2 2 [Host] 26
## 3 NA 22